OpenRaiser/Intern-Atlas huggingface dataset:
A Hierarchical fuzzy controller for a biped robot
Venue: arXiv cs.RO 2014
Year: 2014

In this paper the investigation is placed on the hierarchic neuro-fuzzy systems as a possible solution for biped control. An hierarchic controller for biped is presented, it includes several sub-controllers and the whole structure is generated using the adaptive Neuro-fuzzy method. The proposed hierarchic system focus on the key role that the centre of mass position plays in biped robotics, the system sub-controllers generate their outputs taken into consideration the position of that key point.
UCAT: Ubiquitous Context Awareness Tools for The Blind
Venue: arXiv cs.HC 2014
Year: 2014

Visually impaired people are often confronted with new environments and they find themselves face to face with an innumerous amount of difficulties when facing these environments. Having to surpass and deal with these difficulties that arise with their condition is something that we can help diminish. They are one sense down when trying to understand their surrounding environments and gather information about what is happening around them. Nowadays, mobile devices present significant computing and technological capacity which has been increasing to the point where it is very common for most people to have access to a device with Bluetooth, GPS, Wi-Fi, and both high processing and storage capacities. This allows us to think of applications that can do so much to help people with difficulties. In the particular case of blind people, the lack of visual information can be bypassed with other contextual information retrieved by their own personal devices. Our goal is to provide information to blind users, be able to give them information about the context that surrounds them. We wanted to provide the blind users with the tools to create information and be able to share this information between each other, information about people, locations or objects. Our approach was to split the project into a data and information gathering phase where we did our field search and interviewed and elaborated on how is the situation of environment perception for blind users, followed by a technical phase where we implement a system based on the first stage. Our results gathered from both the collecting phase and our implementing phase showed that there is potential to use these tools in the blind community and that they welcome the possibilities and horizons that it opens them.
An Estimation Method of Measuring Image Quality for Compressed Images of Human Face
Venue: arXiv cs.CV 2014
Year: 2014

Nowadays digital image compression and decompression techniques are very much important. So our aim is to calculate the quality of face and other regions of the compressed image with respect to the original image. Image segmentation is typically used to locate objects and boundaries (lines, curves etc.)in images. After segmentation the image is changed into something which is more meaningful to analyze. Using Universal Image Quality Index(Q),Structural Similarity Index(SSIM) and Gradient-based Structural Similarity Index(G-SSIM) it can be shown that face region is less compressed than any other region of the image.
Checking the strict positivity of Kraus maps is NP-hard
Venue: arXiv cs.CC 2014
Year: 2014

Basic properties in Perron-Frobenius theory are strict positivity, primitivityand irreducibility. Whereas for nonnegative matrices, these properties are equivalent to elementary graph properties which can be checked in polynomial time, we show that for Kraus maps- the noncommutative generalization of stochastic matrices - checking strict positivity (whether the map sends the cone to its interior) is NP-hard. The proof proceeds by reducing to the latter problem the existence of a non-zero solution of a special system of bilinear equations. The complexity of irreducibility and primitivity is also discussed in the noncommutative setting.
Smoothed Model Checking for Uncertain Continuous Time Markov Chains
Venue: arXiv cs.LO 2014
Year: 2014

We consider the problem of computing the satisfaction probability of a formula for stochastic models with parametric uncertainty. We show that this satisfaction probability is a smooth function of the model parameters. This enables us to devise a novel Bayesian statistical algorithm which performs statistical model checking simultaneously for all values of the model parameters from observations of truth values of the formula over individual runs of the model at isolated parameter values. This is achieved by exploiting the smoothness of the satisfaction function: by modelling explicitly correlations through a prior distribution over a space of smooth functions (a Gaussian Process), we can condition on observations at individual parameter values to construct an analytical approximation of the function itself. Extensive experiments on non-trivial case studies show that the approach is accurate and several orders of magnitude faster than naive parameter exploration with standard statistical model checking methods.
How to Apply Assignment Methods that were Developed for Vehicular Traffic to Pedestrian Microsimulations
Venue: arXiv cs.CE 2014
Year: 2014

Applying assignment methods to compute user-equilibrium route choice is very common in traffic planning. It is common sense that vehicular traffic arranges in a user-equilibrium based on generalized costs in which travel time is a major factor. Surprisingly travel time has not received much attention for the route choice of pedestrians. In microscopic simulations of pedestrians the vastly dominating paradigm for the computation of the preferred walking direction is set into the direction of the (spatially) shortest path. For situations where pedestrians have travel time as primary determinant for their walking behavior it would be desirable to also have an assignment method in pedestrian simulations. To apply existing (road traffic) assignment methods with simulations of pedestrians one has to reduce the nondenumerably many possible pedestrian trajectories to a small subset of routes which represent the main, relevant, and significantly distinguished routing alternatives. All except one of these routes will mark detours, i.e. not the shortest connection between origin and destination. The proposed assignment method is intended to work with common operational models of pedestrian dynamics. These - as mentioned before - usually send pedestrians into the direction of the spatially shortest path. Thus, all detouring routes have to be equipped with intermediate destinations, such that pedestrians can do a detour as a piecewise connection of segments on which they walk into the direction of the shortest path. One has then to take care that the transgression from one segment to the following one no artifacts are introduced into the pedestrian trajectory.
Evaluation of YTEX and MetaMap for clinical concept recognition
Venue: arXiv cs.IR 2014
Year: 2014

We used MetaMap and YTEX as a basis for the construc- tion of two separate systems to participate in the 2013 ShARe/CLEF eHealth Task 1[9], the recognition of clinical concepts. No modifications were directly made to these systems, but output concepts were filtered using stop concepts, stop concept text and UMLS semantic type. Con- cept boundaries were also adjusted using a small collection of rules to increase precision on the strict task. Overall MetaMap had better per- formance than YTEX on the strict task, primarily due to a 20% perfor- mance improvement in precision. In the relaxed task YTEX had better performance in both precision and recall giving it an overall F-Score 4.6% higher than MetaMap on the test data. Our results also indicated a 1.3% higher accuracy for YTEX in UMLS CUI mapping.
How Many Beamforming Vectors Generate the Same Beampattern?
Venue: arXiv cs.IT 2014
Year: 2014

In this letter, we address the fundamental question of how many beamforming vectors exist which generate the same beampattern? The question is relevant to many fields such as, for example, array processing, radar, wireless communications, data compression, dimensionality reduction, and biomedical engineering. The desired property of having the same beampattern for different columns of a beamspace transformation matrix (beamforming vectors) often plays a key importance in practical applications. The result is that at most 2^{M-1}-1 beamforming vectors with the same beampattern can be generated from any given beamforming vector. Here M is the dimension of the beamforming vector. At the constructive side, the answer to this question allows for computationally efficient techniques for the beamspace transformation design. Indeed, one can start with a single beamforming vector, which gives a desired beampattern, and generate a number of other beamforming vectors, which give absolutely the same beampattern, in a computationally efficient way. We call the initial beamforming vector as the mother beamforming vector. One possible procedure for generating all possible new beamforming vectors with the same beampattern from the mother beamforming vector is proposed. The application of the proposed analysis to the transmit beamspace design in multiple-input multiple-output radar is also given.
Real Time Industrial Monitoring System
Venue: arXiv cs.OH 2014
Year: 2014

Industries are the biggest workplace all over the world, also there are large number of peoples involves as a worker and most of them are work as a machine operator. There are many systems developed for industrial work place, some of them, monitors machine processes and some do monitoring and control of machine parameters. Such as speed, temperature, production batch count etc. However there is no such system available that provides monitoring of operator during their work is in progress at workplace. This paper proposes the monitoring of the operators and the machines, by Real time Operator -Machine Allocation and monitoring system (Omams). Omams allocates a work machine to worker at entry point itself. It uses automation with RFID and one of the standards of wireless communication method. The system can be industry specific. Through this research paper our approach is to make fair allocation of machine to the operator in industry and reduce hassle for efficiency calculations.
Geodesic Density Tracking with Applications to Data Driven Modeling
Venue: arXiv cs.SY 2014
Year: 2014

Many problems in dynamic data driven modeling deals with distributed rather than lumped observations. In this paper, we show that the Monge-Kantorovich optimal transport theory provides a unifying framework to tackle such problems in the systems-control parlance. Specifically, given distributional measurements at arbitrary instances of measurement availability, we show how to derive dynamical systems that interpolate the observed distributions along the geodesics. We demonstrate the framework in the context of three specific problems: (i) \emph{finding a feedback control} to track observed ensembles over finite-horizon, (ii) \emph{finding a model} whose prediction matches the observed distributional data, and (iii) \emph{refining a baseline model} that results a distribution-level prediction-observation mismatch. We emphasize how the three problems can be posed as variants of the optimal transport problem, but lead to different types of numerical methods depending on the problem context. Several examples are given to elucidate the ideas.
Performance of Hull-Detection Algorithms For Proton Computed Tomography Reconstruction
Venue: arXiv cs.CV 2014
Year: 2014

Proton computed tomography (pCT) is a novel imaging modality developed for patients receiving proton radiation therapy. The purpose of this work was to investigate hull-detection algorithms used for preconditioning of the large and sparse linear system of equations that needs to be solved for pCT image reconstruction. The hull-detection algorithms investigated here included silhouette/space carving (SC), modified silhouette/space carving (MSC), and space modeling (SM). Each was compared to the cone-beam version of filtered backprojection (FBP) used for hull-detection. Data for testing these algorithms included simulated data sets of a digital head phantom and an experimental data set of a pediatric head phantom obtained with a pCT scanner prototype at Loma Linda University Medical Center. SC was the fastest algorithm, exceeding the speed of FBP by more than 100 times. FBP was most sensitive to the presence of noise. Ongoing work will focus on optimizing threshold parameters in order to define a fast and efficient method for hull-detection in pCT image reconstruction.
Active Clustering with Model-Based Uncertainty Reduction
Venue: arXiv cs.LG 2014
Year: 2014

Semi-supervised clustering seeks to augment traditional clustering methods by incorporating side information provided via human expertise in order to increase the semantic meaningfulness of the resulting clusters. However, most current methods are \emph{passive} in the sense that the side information is provided beforehand and selected randomly. This may require a large number of constraints, some of which could be redundant, unnecessary, or even detrimental to the clustering results. Thus in order to scale such semi-supervised algorithms to larger problems it is desirable to pursue an \emph{active} clustering method---i.e. an algorithm that maximizes the effectiveness of the available human labor by only requesting human input where it will have the greatest impact. Here, we propose a novel online framework for active semi-supervised spectral clustering that selects pairwise constraints as clustering proceeds, based on the principle of uncertainty reduction. Using a first-order Taylor expansion, we decompose the expected uncertainty reduction problem into a gradient and a step-scale, computed via an application of matrix perturbation theory and cluster-assignment entropy, respectively. The resulting model is used to estimate the uncertainty reduction potential of each sample in the dataset. We then present the human user with pairwise queries with respect to only the best candidate sample. We evaluate our method using three different image datasets (faces, leaves and dogs), a set of common UCI machine learning datasets and a gene dataset. The results validate our decomposition formulation and show that our method is consistently superior to existing state-of-the-art techniques, as well as being robust to noise and to unknown numbers of clusters.
Appraisal of Social Learning Potentials in Some Trending Mobile Computing Applications
Venue: arXiv cs.CY 2014
Year: 2014

New Mobile technologies have created a new social dimension where individuals can develop increased levels of their social awareness by keeping in touch with old friends, making new friends, dispense new data or product, and getting information in many more aspects of everyday lives, making one to become more knowledgeable which is very beneficial especially for students. Social networks, in particular enable users to share and discuss common interests and provide infrastructures for integrating various user experiences: synchronous and asynchronous communication, game-playing, sharing links and files. The trend of using social networks and social media to deliver and exchange knowledge could bring a new era of social learning in which learners make use all four language skills of reading, writing, listening and speaking. Unlike a traditional e-leaning paradigm with pre-defined curriculum and standard textbooks, social knowledge could be aggregated on demand, just in time, and in context of engaging challenges from social networks, making learning more exciting, social and, game-like experience. Social learning environment engages the learners in discussion, collaboration, exploration, production, discovery and creation. Schools should harness this to develop group collaboration skills and even project based learning activities that span subjects and grade levels. This paper focuses on technologies and avenues of using mobile phones in supporting open social learning among undergraduate students in Nigeria.
Survey on software testing techniques in cloud computing
Venue: arXiv cs.SE 2014
Year: 2014

Cloud computing is the next stage of the internet evolution. It relies on sharing of resources to achieve coherence on a network. It is emerged as new computing standard that impacts several different research fields, including software testing. There are various software techniques used for testing application. It not only changes the way of obtaining computing resources but also changes the way of managing and delivering computing services, technologies and solutions, meanwhile it causes new issues, challenges and needs in software testing. Software testing in cloud can reduce the need for hardware and software resources and offer a flexible and efficient alternative to the traditional software testing process. This paper provides an overview regarding trends, oppurtunities, challenges, issues, and needs in cloud testing and cloud based application.
An EMUSIM Technique and its Components in Cloud Computing- A Review
Venue: arXiv cs.DC 2014
Year: 2014

Recent efforts to design and develop Cloud technologies focus on defining novel methods, policies and mechanisms for efficiently managing Cloud infrastructures. One key challenge potential Cloud customers have before renting resources is to know how their services will behave in a set of resources and the costs involved when growing and shrinking their resource pool. Most of the studies in this area rely on simulation-based experiments, which consider simplified modeling of applications and computing environment. In order to better predict service's behavior on Cloud platforms, an integrated architecture that is based on both simulation and emulation. The proposed architecture, named EMUSIM, automatically extracts information from application behavior via emulation and then uses this information to generate the corresponding simulation model. This paper presents brief overview of the EMUSIM technique and its components. The work in this paper focuses on architecture and operation details of Automated Emulation Framework (AEF), QAppDeployer and proposes Cloud Sim Application for Simulation techniques.
Anomaly Detection Based on Access Behavior and Document Rank Algorithm
Venue: arXiv cs.NI 2014
Year: 2014

Distributed denial of service(DDos) attack is ongoing dangerous threat to the Internet. Commonly, DDos attacks are carried out at the network layer, e.g. SYN flooding, ICMP flooding and UDP flooding, which are called Distributed denial of service attacks. The intention of these DDos attacks is to utilize the network bandwidth and deny service to authorize users of the victim systems. Obtain from the low layers, new application-layer-based DDos attacks utilizing authorize HTTP requests to overload victim resources are more undetectable. When these are taking place during crowd events of any popular website, this is the case is very serious. The state-of-art approaches cannot handle the situation where there is no considerable deviation between the normal and the attackers activity. The page rank and proximity graph representation of online web accesses takes much time in practice. There should be less computational complexity, than of proximity graph search. Hence proposing Web Access Table mechanism to hold the data such as "who accessed what and how many times, and their rank on average" to find the anomalous web access behavior. The system takes less computational complexity and may produce considerable time complexity.
Recommandation mobile, sensible au contexte de contenus évolutifs: Contextuel-E-Greedy
Venue: arXiv cs.AI 2014
Year: 2014

We introduce in this paper an algorithm named Contextuel-E-Greedy that tackles the dynamicity of the user's content. It is based on dynamic exploration/exploitation tradeoff and can adaptively balance the two aspects by deciding which situation is most relevant for exploration or exploitation. The experimental results demonstrate that our algorithm outperforms surveyed algorithms.
Comparison of POD reduced order strategies for the nonlinear 2D Shallow Water Equations
Venue: arXiv cs.NA 2014
Year: 2014

This paper introduces tensorial calculus techniques in the framework of Proper Orthogonal Decomposition (POD) to reduce the computational complexity of the reduced nonlinear terms. The resulting method, named tensorial POD, can be applied to polynomial nonlinearities of any degree $p$. Such nonlinear terms have an on-line complexity of $\mathcal{O}(k^{p+1})$, where $k$ is the dimension of POD basis, and therefore is independent of full space dimension. However it is efficient only for quadratic nonlinear terms since for higher nonlinearities standard POD proves to be less time consuming once the POD basis dimension $k$ is increased. Numerical experiments are carried out with a two dimensional shallow water equation (SWE) test problem to compare the performance of tensorial POD, standard POD, and POD/Discrete Empirical Interpolation Method (DEIM). Numerical results show that tensorial POD decreases by $76\times$ times the computational cost of the on-line stage of standard POD for configurations using more than $300,000$ model variables. The tensorial POD SWE model was only $2-8\times$ slower than the POD/DEIM SWE model but the implementation effort is considerably increased. Tensorial calculus was again employed to construct a new algorithm allowing POD/DEIM shallow water equation model to compute its off-line stage faster than the standard and tensorial POD approaches.
Mining Images in Biomedical Publications: Detection and Analysis of Gel Diagrams
Venue: arXiv cs.IR 2014
Year: 2014

Authors of biomedical publications use gel images to report experimental results such as protein-protein interactions or protein expressions under different conditions. Gel images offer a concise way to communicate such findings, not all of which need to be explicitly discussed in the article text. This fact together with the abundance of gel images and their shared common patterns makes them prime candidates for automated image mining and parsing. We introduce an approach for the detection of gel images, and present a workflow to analyze them. We are able to detect gel segments and panels at high accuracy, and present preliminary results for the identification of gene names in these images. While we cannot provide a complete solution at this point, we present evidence that this kind of image mining is feasible.
Parameterized Directed $k$-Chinese Postman Problem and $k$ Arc-Disjoint Cycles Problem on Euler Digraphs
Venue: arXiv cs.DS 2014
Year: 2014

In the Directed $k$-Chinese Postman Problem ($k$-DCPP), we are given a connected weighted digraph $G$ and asked to find $k$ non-empty closed directed walks covering all arcs of $G$ such that the total weight of the walks is minimum. Gutin, Muciaccia and Yeo (Theor. Comput. Sci. 513 (2013) 124--128) asked for the parameterized complexity of $k$-DCPP when $k$ is the parameter. We prove that the $k$-DCPP is fixed-parameter tractable. We also consider a related problem of finding $k$ arc-disjoint directed cycles in an Euler digraph, parameterized by $k$. Slivkins (ESA 2003) showed that this problem is W[1]-hard for general digraphs. Generalizing another result by Slivkins, we prove that the problem is fixed-parameter tractable for Euler digraphs. The corresponding problem on vertex-disjoint cycles in Euler digraphs remains W[1]-hard even for Euler digraphs.